This post shows how to add visual aspect to Python data analysis workflow in Docker. The code is available at on Github.
And what is Docker?
Docker is an open-source project, which enables containerized and isolated development environments. Docker is relatively new and super hot at the moment since it makes sharing applications and environments easier. Check the project webpage for a general overview.
Adding the visual possibility expands Docker’s possibilities for data analysts. The example in the post is a really simple one. However, the frame can be easily extended to complex analytics and signal processing environments.
No limits for daytime dreamers:
“All men dream: but not equally. Those who dream by night in the dusty recesses of their minds wake up in the day to find it was vanity, but the dreamers of the day are dangerous men, for they may act their dreams with open eyes, to make it possible.” -T.E. Lawrence
I followed a couple of different guides to get everything up and running. Jessie Frazelle’s blog was an inspiration for the whole Docker experiment. Fredrik Averpils blog helped to work in OS X environment. (I am working on OS X El Capitan 10.11.5.) Most of the guides and blogs were for Linux so the insights were truly valuable.
Getting familiar with Docker Containers and practices took some amount of effort. After figuring out how everything worked the process itself is pretty simple. The process can be divided into these steps:
- Install prerequisites (Docker and XQuartz)
- Create Dockerfile (code)
- Build image
- Configure and run image (code)
Here is a screenshot of a graph from a simple Python script.
Works like charm!